



import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=[1, 2, 1],   SCREAMING_SNAKE_CASE_=[2, 2, 4],   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=2.0,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=1E-5,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=10,   SCREAMING_SNAKE_CASE_=8,   )       ->   List[str]:
						UpperCAmelCase_:		Union[str, Any]							      =     parent
						UpperCAmelCase_:		List[str]							      =     batch_size
						UpperCAmelCase_:		str							      =     image_size
						UpperCAmelCase_:		Tuple							      =     patch_size
						UpperCAmelCase_:		List[str]							      =     num_channels
						UpperCAmelCase_:		Union[str, Any]							      =     embed_dim
						UpperCAmelCase_:		int							      =     depths
						UpperCAmelCase_:		int							      =     num_heads
						UpperCAmelCase_:		Dict							      =     window_size
						UpperCAmelCase_:		Dict							      =     mlp_ratio
						UpperCAmelCase_:		Any							      =     qkv_bias
						UpperCAmelCase_:		Tuple							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		List[Any]							      =     drop_path_rate
						UpperCAmelCase_:		Dict							      =     hidden_act
						UpperCAmelCase_:		Tuple							      =     use_absolute_embeddings
						UpperCAmelCase_:		List[Any]							      =     patch_norm
						UpperCAmelCase_:		Union[str, Any]							      =     layer_norm_eps
						UpperCAmelCase_:		Optional[Any]							      =     initializer_range
						UpperCAmelCase_:		List[str]							      =     is_training
						UpperCAmelCase_:		Tuple							      =     scope
						UpperCAmelCase_:		Union[str, Any]							      =     use_labels
						UpperCAmelCase_:		List[str]							      =     type_sequence_label_size
						UpperCAmelCase_:		Any							      =     encoder_stride

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Dict							      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

						UpperCAmelCase_:		Optional[int]							      =     None
						if self.use_labels:
									UpperCAmelCase_:		int							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)

						UpperCAmelCase_:		Optional[Any]							      =     self.get_config()

						return config, pixel_values, labels

			def      __snake_case       (self					)       ->   List[str]:
						return SwinvaConfig(
						    image_size=self.image_size,   patch_size=self.patch_size,   num_channels=self.num_channels,   embed_dim=self.embed_dim,   depths=self.depths,   num_heads=self.num_heads,   window_size=self.window_size,   mlp_ratio=self.mlp_ratio,   qkv_bias=self.qkv_bias,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   drop_path_rate=self.drop_path_rate,   hidden_act=self.hidden_act,   use_absolute_embeddings=self.use_absolute_embeddings,   path_norm=self.patch_norm,   layer_norm_eps=self.layer_norm_eps,   initializer_range=self.initializer_range,   encoder_stride=self.encoder_stride,   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		Optional[Any]							      =     SwinvaModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Tuple							      =     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
						UpperCAmelCase_:		Any							      =     int(config.embed_dim * 2 ** (len(config.depths					) - 1)					)

						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, expected_seq_len, expected_dim)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     SwinvaForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(
						    result.logits.shape,   (self.batch_size, self.num_channels, self.image_size, self.image_size)					)

						# test greyscale images
						UpperCAmelCase_:		Optional[int]							      =     1
						UpperCAmelCase_:		Dict							      =     SwinvaForMaskedImageModeling(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()

						UpperCAmelCase_:		Optional[int]							      =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, 1, self.image_size, self.image_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		Optional[int]							      =     self.type_sequence_label_size
						UpperCAmelCase_:		List[Any]							      =     SwinvaForImageClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[int]							      =     model(SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.type_sequence_label_size)					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Tuple							      =     self.prepare_config_and_inputs()
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     config_and_inputs
						UpperCAmelCase_:		Tuple							      =     {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
			)
			A      		=	(
			    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Tuple							      =     SwinvaModelTester(self					)
						UpperCAmelCase_:		int							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   embed_dim=37					)

			def      __snake_case       (self					)       ->   int:
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Any							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""					)
			def      __snake_case       (self					)       ->   Optional[Any]:
						pass

			@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""					)
			def      __snake_case       (self					)       ->   Tuple:
						pass

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Union[str, Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									self.assertIsInstance(model.get_input_embeddings(),   (nn.Module)					)
									UpperCAmelCase_:		str							      =     model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_,   nn.Linear					)					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Dict							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_:		int							      =     [*signature.parameters.keys()]

									UpperCAmelCase_:		Union[str, Any]							      =     ["""pixel_values"""]
									self.assertListEqual(arg_names[:1],   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_:		Any							      =     True

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		List[Any]							      =     True
									UpperCAmelCase_:		List[Any]							      =     False
									UpperCAmelCase_:		Dict							      =     True
									UpperCAmelCase_:		List[Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									model.to(SCREAMING_SNAKE_CASE_					)
									model.eval()
									with torch.no_grad():
												UpperCAmelCase_:		Tuple							      =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)
									UpperCAmelCase_:		Optional[int]							      =     outputs.attentions
									UpperCAmelCase_:		Union[str, Any]							      =     len(self.model_tester.depths					)
									self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									UpperCAmelCase_:		Optional[Any]							      =     True
									UpperCAmelCase_:		List[str]							      =     config.window_size**2
									UpperCAmelCase_:		Union[str, Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									model.to(SCREAMING_SNAKE_CASE_					)
									model.eval()
									with torch.no_grad():
												UpperCAmelCase_:		Optional[Any]							      =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)
									UpperCAmelCase_:		Dict							      =     outputs.attentions
									self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]					),   [self.model_tester.num_heads[0], window_size_squared, window_size_squared],   )
									UpperCAmelCase_:		Any							      =     len(SCREAMING_SNAKE_CASE_					)

									# Check attention is always last and order is fine
									UpperCAmelCase_:		Tuple							      =     True
									UpperCAmelCase_:		str							      =     True
									UpperCAmelCase_:		Union[str, Any]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									model.to(SCREAMING_SNAKE_CASE_					)
									model.eval()
									with torch.no_grad():
												UpperCAmelCase_:		Any							      =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)

									if hasattr(self.model_tester,   """num_hidden_states_types"""					):
												UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.num_hidden_states_types
									else:
												# also another +1 for reshaped_hidden_states
												UpperCAmelCase_:		int							      =     2
									self.assertEqual(out_len + added_hidden_states,   len(SCREAMING_SNAKE_CASE_					)					)

									UpperCAmelCase_:		str							      =     outputs.attentions

									self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]					),   [self.model_tester.num_heads[0], window_size_squared, window_size_squared],   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Any:
						UpperCAmelCase_:		List[str]							      =     model_class(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()

						with torch.no_grad():
									UpperCAmelCase_:		str							      =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)

						UpperCAmelCase_:		Any							      =     outputs.hidden_states

						UpperCAmelCase_:		Optional[Any]							      =     getattr(
						    self.model_tester,   """expected_num_hidden_layers""",   len(self.model_tester.depths					) + 1					)
						self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

						# Swinv2 has a different seq_length
						UpperCAmelCase_:		List[Any]							      =     (
						    config.patch_size
						    if isinstance(config.patch_size,   collections.abc.Iterable					)
						    else (config.patch_size, config.patch_size)
						)

						UpperCAmelCase_:		List[Any]							      =     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]					),   [num_patches, self.model_tester.embed_dim],   )

						UpperCAmelCase_:		int							      =     outputs.reshaped_hidden_states
						self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     reshaped_hidden_states[0].shape
						UpperCAmelCase_:		Optional[int]							      =     (
						    reshaped_hidden_states[0].view(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   height * width					).permute(0,   2,   1					)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]					),   [num_patches, self.model_tester.embed_dim],   )

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		List[Any]							      =     (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size,   collections.abc.Iterable					)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		int							      =     True
									self.check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCAmelCase_:		Union[str, Any]							      =     True

									self.check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_:		int							      =     3

						UpperCAmelCase_:		Optional[int]							      =     (
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size,   collections.abc.Iterable					)
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						UpperCAmelCase_:		Optional[Any]							      =     (
						    config.patch_size
						    if isinstance(config.patch_size,   collections.abc.Iterable					)
						    else (config.patch_size, config.patch_size)
						)

						UpperCAmelCase_:		int							      =     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						UpperCAmelCase_:		Optional[Any]							      =     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		List[str]							      =     True
									self.check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   (padded_height, padded_width)					)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									UpperCAmelCase_:		List[str]							      =     True
									self.check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   (padded_height, padded_width)					)

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   str:
						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		Optional[int]							      =     SwinvaModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs_for_common()

						UpperCAmelCase_:		List[Any]							      =     _config_zero_init(SCREAMING_SNAKE_CASE_					)
						for model_class in self.all_model_classes:
									UpperCAmelCase_:		Optional[Any]							      =     model_class(config=SCREAMING_SNAKE_CASE_					)
									for name, param in model.named_parameters():
												if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item(),   [0.0, 1.0],   msg=f'Parameter {name} of model {model_class} seems not properly initialized',   )







@require_vision
@require_torch
class 				_a				(   unittest.TestCase					):

			@cached_property
			def      __snake_case       (self					)       ->   int:
						return (
						    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					)
						    if is_vision_available()
						    else None
						)

			@slow
			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Dict							      =     SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""					).to(
						    SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     self.default_image_processor

						UpperCAmelCase_:		List[Any]							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
						UpperCAmelCase_:		List[str]							      =     image_processor(images=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE_					)

						# forward pass
						with torch.no_grad():
									UpperCAmelCase_:		Dict							      =     model(**SCREAMING_SNAKE_CASE_					)

						# verify the logits
						UpperCAmelCase_:		Tuple							      =     torch.Size((1, 1000)					)
						self.assertEqual(outputs.logits.shape,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]					).to(SCREAMING_SNAKE_CASE_					)
						self.assertTrue(torch.allclose(outputs.logits[0, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)











from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


a      :  Dict			     =			logging.get_logger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''pixel_values''']

			def __init__(self,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 1 / 255,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   None:
						super().__init__(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     size if size is not None else {"""height""": 256, """width""": 256}
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						UpperCAmelCase_:		Dict							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		int							      =     do_resize
						UpperCAmelCase_:		Any							      =     size
						UpperCAmelCase_:		List[Any]							      =     resample
						UpperCAmelCase_:		int							      =     do_center_crop
						UpperCAmelCase_:		Dict							      =     crop_size
						UpperCAmelCase_:		Tuple							      =     do_rescale
						UpperCAmelCase_:		Tuple							      =     rescale_factor
						UpperCAmelCase_:		Dict							      =     do_normalize
						UpperCAmelCase_:		Dict							      =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCAmelCase_:		int							      =     image_std if image_std is not None else IMAGENET_STANDARD_STD

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = PIL.Image.BICUBIC,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Tuple							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return resize(
						    SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   resample=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						if "height" not in size or "width" not in size:
									raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(SCREAMING_SNAKE_CASE_,   size=(size["""height"""], size["""width"""]),   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   int:
						return rescale(SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   np.ndarray:
						return normalize(SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_,   data_format=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST,   **SCREAMING_SNAKE_CASE_,   )       ->   PIL.Image.Image:
						UpperCAmelCase_:		Any							      =     do_resize if do_resize is not None else self.do_resize
						UpperCAmelCase_:		Dict							      =     resample if resample is not None else self.resample
						UpperCAmelCase_:		Tuple							      =     do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCAmelCase_:		str							      =     do_rescale if do_rescale is not None else self.do_rescale
						UpperCAmelCase_:		List[Any]							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_normalize if do_normalize is not None else self.do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean if image_mean is not None else self.image_mean
						UpperCAmelCase_:		Dict							      =     image_std if image_std is not None else self.image_std

						UpperCAmelCase_:		Any							      =     size if size is not None else self.size
						UpperCAmelCase_:		Optional[Any]							      =     get_size_dict(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     crop_size if crop_size is not None else self.crop_size
						UpperCAmelCase_:		Optional[int]							      =     get_size_dict(SCREAMING_SNAKE_CASE_,   param_name="""crop_size"""					)

						UpperCAmelCase_:		str							      =     make_list_of_images(SCREAMING_SNAKE_CASE_					)

						if not valid_images(SCREAMING_SNAKE_CASE_					):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""					)

						if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""					)

						if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""					)

						if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""					)

						if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""					)

						# All transformations expect numpy arrays.
						UpperCAmelCase_:		Tuple							      =     [to_numpy_array(SCREAMING_SNAKE_CASE_					) for image in images]

						if do_resize:
									UpperCAmelCase_:		int							      =     [self.resize(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_,   resample=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_center_crop:
									UpperCAmelCase_:		Dict							      =     [self.center_crop(image=SCREAMING_SNAKE_CASE_,   size=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_rescale:
									UpperCAmelCase_:		Optional[int]							      =     [self.rescale(image=SCREAMING_SNAKE_CASE_,   scale=SCREAMING_SNAKE_CASE_					) for image in images]

						if do_normalize:
									UpperCAmelCase_:		int							      =     [self.normalize(image=SCREAMING_SNAKE_CASE_,   mean=SCREAMING_SNAKE_CASE_,   std=SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					) for image in images]

						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_,   tensor_type=SCREAMING_SNAKE_CASE_					)






